Adaptive weighting of pattern features during learning

Irrelevant or redundant features may have negative effects on classification algorithms. The designer of a classification algorithm typically require a few very significant features characterising the class membership of the patterns. The discriminatory information is encoded in a very complex manner and features which are the most important for pattern classification may not be apparent. One way to address this problem is the use of feature weighting procedure as a data preprocessing step. In this paper we propose a two-step algorithm as an extension of the learning vector quantization algorithm (LVQ). This approach is based on weighting features depending on their contribution to discrimination. Adapting weighting coefficients and codewords is done simultaneously by using a new global learning algorithm named ωLVQ2. Experiments are undertaken on a synthetic problem and on real problems in speech and speaker recognition domain, to show significant improvement over the standard learning algorithm